You trained a model, got promising results and now you need to share the dataset, the notebook and the weights with your collaborator at another university. The CSV is 4GB. The Parquet files total 12GB. The Jupyter notebook has inline visualizations that only render properly in the right environment. And the model checkpoint is 800MB of binary data that no email service will accept. Here is how data scientists handle file sharing without losing reproducibility.
The Problem with Sharing Large Datasets
Data science workflows produce large files by nature. A moderately sized dataset in CSV format can reach several gigabytes. When you need to share this work with a colleague or reviewer, the usual tools fall apart.
Email tops out at 25MB. Slack compresses binary files. Google Drive has storage quotas that fill up fast. GitHub has a 100MB limit. What data scientists need is a way to upload a large file, get a link and send it. No accounts for the recipient. No quotas. No compression that corrupts binary formats.
Sharing CSV and Tabular Data
CSV remains the universal language of tabular data. Every tool reads it. Every platform imports it. The problem is that CSV files are verbose. A dataset with 10 million rows and 50 columns can exceed 5GB.
Upload large CSV files to EasySend's CSV sharing and send the link. The file transfers byte-for-byte with no modification, preserving encoding, delimiters and line endings. This matters because a CSV that works on your Linux machine can break on a colleague's Windows setup if the transfer tool silently converts line endings.
For compressed columnar data, Parquet file sharing keeps the format intact. Parquet files are already compressed, transfer faster than equivalent CSVs and preserve schema information that CSV throws away.
Jupyter Notebooks Are More Than Code
A Jupyter notebook combines code, output, visualizations, markdown documentation and sometimes interactive widgets. The .ipynb format stores all of this in a JSON structure that is surprisingly fragile when shared through the wrong channels.
Copy-pasting cells into an email destroys the output. Messaging apps sometimes corrupt the JSON structure. Some cloud storage services modify file encoding during upload, breaking cell outputs that contain special characters or binary image data.
Sharing Jupyter notebooks through EasySend preserves the complete .ipynb file without modification. Your collaborator opens it in JupyterLab or VS Code and sees exactly what you saw. For reproducibility, this is critical. A notebook with missing outputs undermines the entire point of documented research.
Model Weights and Checkpoints
Trained model weights are pure binary data. A fine-tuned transformer can produce checkpoints ranging from hundreds of megabytes to tens of gigabytes. These files are not human-readable and cannot be previewed. They just need to arrive intact.
Many file sharing services treat large binary files as second-class citizens with size limits, throttled speeds or upload timeouts. When you are sharing a 6GB model checkpoint, you need a service that handles large binary transfers as a core feature.
Sharing AI model files through EasySend supports large uploads with no throttling and no compression. Upload the .pt, .h5, .onnx or .safetensors file, share the link and your collaborator pulls it down at full speed.
Reproducibility Depends on Exact Transfers
In data science, reproducibility is everything. A paper is only credible if someone else can take your data, run your code and get the same results. This chain breaks the moment a file gets modified during transfer.
Silent modifications happen more often than you might expect. Some services re-encode text files or strip metadata from binary files. A practical verification workflow: generate a SHA-256 hash before uploading, share through EasySend, have your collaborator hash the download and compare.
Automating Dataset Distribution with the API
Sharing files manually works for occasional transfers. But data scientists who distribute datasets regularly to students, team members or external collaborators need automation.
The EasySend developer API lets you script the entire process. A Python script can upload a dataset, generate a share link and post it to Slack without manual intervention.
A typical workflow: your training pipeline finishes, a post-training script uploads the dataset and weights via the API, generates share links with expiration dates and notifies your team on Slack. For researchers managing ongoing projects, each upload gets its own link so you can version datasets by date and collaborators always know which version they are working with.
Practical Tips
- Name files with version info - Include the date or experiment ID. "dataset_v3_20260403.parquet" beats "data_final_final.csv".
- Include a README - Upload a text file explaining the schema, collection method and preprocessing steps.
- Set expiration dates - For peer review datasets, expire links after the review period to prevent outdated data from circulating.
- Use Parquet over CSV when possible - It preserves types, compresses better and loads faster.
- Share notebooks with outputs included - Clear outputs only if file size is the bottleneck. Your collaborator should not have to re-run every cell just to see results.
Data science generates files that are too large for email, too specialized for generic cloud storage and too important for unreliable transfers. A workflow built around direct uploads, exact byte-for-byte transfers and API automation removes the friction so you can focus on the research.